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A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms Noor Salah Hassan & Nawzat Sadiq Ahmed

A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms

Author (s)

Noor Salah Hassan & Nawzat Sadiq Ahmed

Abstract

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The detection of the tumor region in medical images such as (MRI, CT, X-Ray) a boring and time-consuming task is by radiologists or experts. So, in this review the accuracy is needed for detection the tumor. The area of medical imaging is also reducing complexity and improving diagnostic precision with the growth of information technology. This review paper makes a comparison between the k-mean, and fuzzy c-mean algorithms to display the results and accuracy of them to detection the brain tumor. The execution of the k-mean algorithm is based on centroid, size, split process, threshold, epoch, characteristics, and number of iterations, while Fuzzy C-mean is executed on the basis of the fuzziness value and the termination condition in medical images. In comparing the efficiency parameters with the state-of-the-art processes, the experimental outcomes demonstrate the importance of medical images (MRI, CT and X-Ray) and the accuracy of each algorithm that have been discussed.

 Keywords: Brain Tumor, Medical Images, Pre-processing, Segmentation, Feature Extraction, K- means.

 

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Title: A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms
Author: Noor Salah Hassan & Nawzat Sadiq Ahmed
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4596362
Media: Online
Volume: 5
Issue: 6
Acceptance Date: 07/03/2021
Date of Publication: 11/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/743.pdf
Free download: Available
Page: 21-32
First Page: 21
Last Page: 32
Paper Type: Literature review
Current Status: Published

 

Cite This Article:

Noor Salah Hassan & Nawzat Sadiq Ahmed (2021). A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms. International Journal of Science and Business, 5(6), 21-32. doi: https://doi.org/10.5281/zenodo.4596362

Retrieved from https://ijsab.com/wp-content/uploads/743.pdf

 

About Author (s)

Noor Salah Hassan (corresponding author), Department of Information Technology, Akre Technical Collage, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email:  noor.salah.hassan6@gmail.com

Nawzat Sadiq Ahmed, Department of Information Technology, Technical Collage of Administration, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email:  nawzat.ahmed@dpu.edu.krd.

 

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DOI: https://doi.org/10.5281/zenodo.4596362

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A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors Shler Farhad Khorshid & Nawzat Sadiq Ahmed

A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors

Author (s)

Shler Farhad Khorshid & Nawzat Sadiq Ahmed

Abstract

­

Brain tumor is one of the commonest tumors. For the diagnosis of this disease, automated detection and classification are crucial. Magnetic resonance imaging (MRI) is a unique sort of imaging which is utilized for detecting these tumors and categorizing them as benign or malignant using special algorithms such as of K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). The classification of brain tumors through imaging can be divided into four phases: pre-processing, extraction, segmentation and classification. This paper reviews some recent studies that highlight the efficacy of K-NN and SVM accuracies in classifying brain MRI images as normal or abnormal, benign or malignant.

 Keywords: brain tumor, Magnetic resonance imaging (MRI), classification, SVM, K-NN.

 

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Title: A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors
Author: Shler Farhad Khorshid & Nawzat Sadiq Ahmed
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4590054
Media: Online
Volume: 5
Issue: 6
Acceptance Date: 05/03/2021
Date of Publication: 09/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/742.pdf
Free download: Available
Page: 12-20
First Page: 12
Last Page: 20
Paper Type: Literature review
Current Status: Published

 

Cite This Article:

Shler Farhad Khorshid & Nawzat Sadiq Ahmed (2021). A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors. International Journal of Science and Business, 5(6), 12-20. doi: https://doi.org/10.5281/zenodo.4590054

Retrieved from https://ijsab.com/wp-content/uploads/742.pdf

 

About Author (s)

Shler Farhad Khorshid (corresponding author), Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. E-mail: Shler.sulayvani@gmail.com

Nawzat Sadiq Ahmed, Information Technology Management, Technical College of Administration, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email: nawzat.ahmed@dpu.edu.krd

 

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DOI: https://doi.org/10.5281/zenodo.4590054